Systems and methods for assessing standards for mobile image quality

ABSTRACT

Methods are provided for defining and determining a formal and verifiable mobile document image quality and usability (MDIQU) standard, or Standard for short. The Standard ensures that a mobile image can be used in an appropriate mobile document processing application, for example an application for mobile check deposit. In order to quantify the usability, the Standard establishes 5 quality and usability grades. A mobile image is first tested to determine if the quality is sufficient to obtain content from the image by performing multiple different image quality assessment tests. If the image quality is sufficient, one or more document usability computations are made to determine if the document or content in the image is usable by a particular application. A ranking is then assigned to the image based on the results of the tests which is indicative of the quality and usability of the image.

RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 14/217,170, filed on Mar. 17, 2014, which is which claimspriority to provisional application No. 61/802,039, filed on Mar. 15,2013, all which are hereby incorporated by reference in their entirety.

BACKGROUND

1. Field of the Invention

The embodiments described herein relate to processing images capturedusing a mobile device, and more particularly to developing standards forprocessing the images and assessing an image processing system todetermine whether it meets the standards.

2. Related Art

The use of mobile devices to capture images is ubiquitous in modernculture. Aside from taking pictures of people, places and events, usersare utilizing the cameras on their devices for many different purposes.One of those purposes is capturing images of content that the user laterwants to review, such as a description of a product, a document, licenseplate, etc.

Financial institutions and other businesses have become increasinglyinterested in electronic processing of checks and other financialdocuments in order to expedite processing of these documents. Sometechniques allow users to scan a copy of the document using a scanner orcopier to create an electronic copy of the document that can bedigitally processed to extract content. This provides added convenienceover the use of a hardcopy original, which would otherwise need to bephysically sent or presented to the recipient for processing. Forexample, some banks can process digital images of checks and extractcheck information from the image needed to process the check for paymentand deposit without requiring that the physical check be routedthroughout the bank for processing.

Mobile devices that incorporate cameras are ubiquitous and may also beuseful to capture images of financial documents for mobile processing offinancial information. The mobile device may be connected with afinancial institution or business through a mobile network connection.However, the process of capturing and uploading images of financialdocuments using the mobile device is often prone to error, producingimages of poor quality which cannot be used to extract data. The user isoften unaware of whether the captured document image is sufficient andcapable for processing by a business or financial institution.

Attempts have been made to improve the quality of mobile images offinancial documents to improve the accuracy of information extractedtherefrom. There are numerous ways in which an image can be improved forextracting its content, some of which are implemented individually andsome of which are implemented together. However, it is difficult todetermine which methods are the best at improving image quality andcontent extraction. Of the methods often used, it is even more difficultto select a threshold of that particular method which will provide anaccurate capture in as little time as possible. Finally, determiningwhether an image processing system is capable of performing adequateimage capture, processing and content extraction has not been explored.

Therefore, there is a need for identifying image processing techniqueswhich will provide optimal image correction and accurate contentextraction.

SUMMARY

Embodiments described herein provide methods for defining anddetermining a formal and verifiable mobile document image quality andusability (MDIQU) standard, or Standard for short. The goal of thisStandard is to ensure that a mobile image can be used in an appropriatemobile document processing application, for example an application formobile check deposit, mobile bill pay, mobile balance transfer or mobileinsurance submission and application. In order to quantify theusability, the Standard establishes 5 quality and usability grades: thehigher grade images will tend to produce higher accuracy results in therelated “mobile” application. A mobile image is first tested todetermine if the quality is sufficient to obtain content from the imageby performing multiple different image quality assessment tests. If theimage quality is sufficient, one or more document usability computationsare made to determine if the document or content in the image is usableby a particular application. A ranking is then assigned to the imagebased on the results of the tests which is indicative of the quality andusability of the image.

Systems and methods are provided for developing standards of imageprocessing for mobile image capture and assessing whether mobileprocessing engines meet the standards. A mobile processing engine (MDE)is evaluated to determine if it can perform one or more technicalcapabilities for improving the quality of and extracting content from animage of a financial document (such as a check). A verification processthen begins, where the MDE performs the image quality enhancements andtext extraction steps on sets of images from a test deck of good and badimages of financial documents with known content. The results of theprocessing of the test deck are then evaluated by comparing confidencelevels with thresholds to determine if each set of images should beaccepted or rejected. Further analysis determines whether any of thesets of images were falsely accepted or rejected, and an overall errorrate is computed. The overall error rate is then compared with minimumaccuracy criteria, and if the criteria are met, the MDE meets thestandard for mobile deposit

The goal of this Standard is to ensure that a mobile image can be usedin an appropriate mobile document processing application, primarilyMobile Deposit [1], Mobile Bill Pay [2], Mobile Balance Transfer [3] andMobile Insurance [4].

Other features and advantages should become apparent from the followingdescription of the preferred embodiments, taken in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments disclosed herein are described in detail withreference to the following figures. The drawings are provided forpurposes of illustration only and merely depict typical or exemplaryembodiments. These drawings are provided to facilitate the reader'sunderstanding and shall not be considered limiting of the breadth,scope, or applicability of the embodiments. It should be noted that forclarity and ease of illustration these drawings are not necessarily madeto scale.

FIG. 1 illustrates a method of assessing, grading and ranking an imageto determine an image quality and an image usability, according to oneembodiment of the invention.

FIG. 2 illustrates a system for assessing, grading and ranking the imageutilizing a mobile image capture device and image server, according toone embodiment of the invention.

FIG. 3 is an image illustrating an example of an out-of-focus (OOF)image identified during an OOF image quality test, according to oneembodiment of the invention.

FIGS. 4A and 4B are images of plain skew and view skew images,respectively, identified during a skew image quality test, according toone embodiment of the invention.

FIGS. 5A and 5B are images of a low contrast image and poor darknessimage, respectively, identified during a low internal contrast imagequality test, according to one embodiment of the invention.

FIG. 6A is an image of a document where the corners are cut off, aswould be identified during a cut corner image quality test, according toone embodiment of the invention.

FIG. 6B is an image of a warped document which would be identifiedduring a warpage image quality test, according to one embodiment of theinvention.

FIG. 7 is a block diagram that illustrates an embodiment of acomputer/server system upon which an embodiment of the inventivemethodology may be implemented

The various embodiments mentioned above are described in further detailwith reference to the aforementioned figured and the following detaileddescription of exemplary embodiments.

DETAILED DESCRIPTION I. Summary of Mobile Deposit Standards

Embodiments described herein define formal and verifiable mobiledocument image quality and usability (MDIQU) standard, of Standard forshort. The goal of this Standard is to ensure that a mobile image can beused in an appropriate mobile document processing application, primarilymobile check deposit, mobile bill pay, mobile balance transfer or mobileinsurance submission and application, although this list is onlyillustrative of the types of images and documents which may takeadvantage of the methods.

In order to quantify the usability, the Standard establishes 5 qualityand usability grades: the higher grade images will tend to producehigher accuracy results in the related “mobile” application. Thestandards for various mobile document processing applications may usethe ranking system established by this invention in order to define thevarious accuracy levels. For example, a mobile deposit standard mayrequire MICR accuracy of 99% on mobile images of checks which are Grade1 (the highest) according to this Standard, 97% on Grade 2 mobile imagesetc. The lowest grade (or 2 lowest grades, depending on applicationpreferences) defines unusable images for which no meaningful predictionof accuracy could be made.

Given an application (e.g. Mobile Deposit) and a mobile image, MDIQUanswers two basic questions:

1. Does the mobile image have good image quality?

2. Does the image contain a document which is valid for the particularapplication it's intended for (a valid US check in case of MD)?

Moreover, if the image quality is characterized as bad, MDIQU willdetect a particular quality defect.

The answer on the first question (Image Quality) doesn't depend on thetype of document or application in question. All that MDIQU is supposedto verify is that a human would easily read the relevant data off themobile image. For example, a good quality mobile image of a check mustcontain the entire check (all 4 corners), be high contrast/sharp withall the characters being legible. The same will apply to mobile imagesof a bills, driver's licenses etc.

The answer on the second question (Document Validity) is supposed toensure that the document within the image is indeed a check, a bill, adriver's license etc. Therefore, the validity factor is defined for eachapplication independently.

II. MDIQU System and Workflow

FIG. 1 illustrates a flowchart of a workflow for assessing mobile imagequality standards, according to one embodiment of the invention. Thesteps may include:

100—accept mobile image under examination and an expected document type

200—compute individual IQA scores

300—output individual IQA scores

400—compute compound IQA scores (see Section 4)

450—output compound IQA scores

600—make decision if image quality is sufficient to proceed

700—if “No”, assign image the lowest rank and exit workflow (in thiscase, the document can't be even extracted from the image).

750—if “Yes,” receive document category and optionally set of criticalfields

800—compute usability score from document category and critical fields

850—output document usability score

900—compares usability score to pre-defined threshold

1000—if score is low, assign Rank 3 of Rank 4, then exit.

1100—if score is high, assign Rank 1 of Rank 2, then exit.

One embodiment of an MDIQU system is illustrated in FIG. 2, where amobile image capture device 102 with an image quality assurance database104 is connected with an image server 106 with a document validitydatabase 108. Thus, the mobile device 102 may perform one or more of theIQA steps based on information stored in the IQA DB 104 to determine animage quality score, after which the image (if sufficient) is sent tothe image server for performing a document validity test usinginformation stored in the document validity DB 108. An image with asufficient quality and usability may then be transmitted to anapplication server 110 where an application will utilize the image andits content for a particular task. Although not illustrated herein, thesystem may also perform the IQA tests at the image server 106, and evenperform the document usability tests at the mobile device 102.

III. Individual Mobile Image Quality Tests

In one embodiment, the MDIQU system performs the following image qualitytests (Mobile IQA tests) to detect the following image deficiencies:Out-of-focus (OOF), Shadow, Small size, View Skew (perspectivedistortion), Plain Skew, Warped, Low Internal Contrast, Reflection, CutCorners, Cut Sides, Busy Background and Low External Contrast. Eachscore should be returned on the scale of 0 to 1000, when 0 means “Nodefect” and 1000 means “Severe Defect”. Alternately, MDIQU system maydetect absence of the defects, thus swapping 0 and 1000.

U.S. Pat. No. 8,582,862, issued Nov. 12, 2013, gives a description ofthe Mobile IQA tests and corresponding algorithms, and is hereinincorporated by reference in its entirety. Below is a brief descriptionof IQA tests along with their relative importance.

IQA Score Importance How it gets computed OOF High for all OOF iscomputed on grey-scale snippets of documents based applications onfrequencies of high-contrast local areas. The more high- contrast areasis found, the higher score becomes (1000 means “absolutely sharp”)Shadowed High for “Shadowed” score is computed on grayscale snippets ofMD, documents. The engine tries to break the entire snippet into 2Medium areas of very different brightness. For each such otherwiserepresentation (could be several), the engine takes into account thedelta in brightness and the size of the “darker” area. If the delta ishigh enough and the size of “darker” area is close to 50%, a “perfect”shadow is registered, making the score = 0. 1000 means “no shadow”Reflection High for Thus IQA score was designed to detect glare on theimage and Insurance, assign a numeric value to its size so that 0 wouldmean “no low glare”, and 1000 would mean “significant” glare otherwiseLow Medium Firstly, the engine computes the brightness histogram usingInternal grayscale snippet of the document. The histogram is a 256-Contrast values vector H[ ], where H[0] is the number of pixels withbrightness 0 (absolutely black) . . . up to H[255] is the number ofpixels with brightness 0 (absolutely white). Secondly, having computedthe histogram H, the engine finds out where ⅓ of all pixels are locatedcounting from 0 upwards (G1) and from 255 downwards (G2). Then weightedaverages of brightness are computed for [0, G1] and [G2, 255], whichcould be considered average “darkness” (D) and average “lightness” (L)of the snippet respectively. The difference L-D describes the contrast.After proper normalization, 0 means very poor contrast and 1000 meansvery high contrast Darkness Medium The goal of this score is to ensurethat the image is not too dark.. Firstly, the engine computes thebrightness histogram using grayscale snippet of the document asexplained above. Then a weighted average grayscale value (G) iscomputed. After proper normalization G = 255 is translated into 1000(document is pure white), G = 0 is translated into 0 (absolutely black)Plain Low This IQA reflects 2D skew of the document (no skew means Skewthe document's sides are parallel to the image sides). Afternormalization, 1000 means 0 degrees skew while 0 means 45 degrees (45degrees is the maximum: further 2D rotation will mean that the documentchanged orientation) .The importance of detection view skew is low asMoobile SDK handles hight degree of 2D skew nicely. View Mid This IQAreflects 3D (view) angle between the camera and the Skew document Noview angle means that the camera view is perpendicular to the thedocument surface. When the view angle becomes significant, the documentbecomes perspectively distorted. After normalization, 1000 means 0degrees skew while 0 means 22 degrees, Cut High This IQA reflectspresence of all four corners of the document Corners within the mobileimage. The engine finds out if a corner is (side) located outside of theimage as part of the automatic framing/cropping process. Depending onhow big is the missing (cut-off) part of the corner, the engine computesa penalty per corner. Zero penalty means that the corner resides insidethe image, maximum penalty is assigned when at least 25% (by area) ofthe image quadrant related to this corner is cut-off. Then maximum of 4penalties computed for all for corners becomes the overall cut-cornerpenalty. After normalization, 1000 means that no penalty was computedfor all corners; 0 means that at least 1 (unspecified) corner waspenalized by max. penalty (see above) Small Low This score is computedby comparing the document snippet Image size against the mobile imagesize (the latter becomes known after cropping). When the camera movesaway from the document, the snippet becomes smaller, thus causing lowvalues of this IQA metric. After normalization, the score of 1000 meansthat the document occupies at least ⅓^(rd) of the entire mobile imagearea, while 0 means that it occupies less than ⅙^(th) of the entiremobile image area. The importance of detection Small Image is low asMoobile SDK handles even small images quite nicely. Warpage None for Thescore is computed as a measure of how flat the document DLs, is. The wayto compute that is to measure how far the snippet Medium sides deviatefrom ideal straight lines. To normalize such otherwise deviation, itsmaximum value along side is divided by the perpendicular side's length.For example, to compute “flatness” along the upper horizontal side ofthe document, the largest deviation (in pixels) is divided by thedocument snippet height. In the end, the largest normalized deviation istaken, and then further normalized to become IQA: if the document isabsolutely flat, the score becomes 1000; the deviation of 25% and morebecomes 0. Low High for There is insufficient contrast between thedocument and the External DLs, background (making it harder to locatedthe document) Contrast otherwise medium Busy High for Too manycontrasting objects detected in the background Background DLs, (makingit harder to located the document) otherwise medium

Examples of Individual Mobile Defects

FIG. 3 illustrates an image which is out of focus (OOF) and would beidentified with an OOF IQA test. The algorithm may be modified to becomputed only “on characters”. This means that a special filter is usedto detect positions of text characters, which then are used in thecomputation while the other areas are ignored. FIG. 3 illustrates anexample of poor OOF (OOF score=90).

Reflection IQA testing doesn't work well on the image level and needs tobe “localized” to the field level.

FIG. 4A illustrates an image of a document with poor plain skew(IQA=200), while FIG. 4B illustrates an image of a document with a viewskew.

FIG. 5A illustrates an image with low contrast (specifically a lowcontrast IQA=210). This IQA should not be confused with Low Contrastbackground: the former describes contrasts inside the document whereasthe latter outside (between the document and background). FIG. 5Billustrates an image which suffers from darkness, and therefore has adarkness IQA score of 290.

FIG. 6A illustrates an image of a document with cut corners, receiving ascore of 900. FIG. 6B illustrates an image of a document with a warpedimage, receiving a warpage score of 0.

These IQA scores may be averaged or combined to determine a total scoreor weighted depending on the relevance of a particular IQA test.Nevertheless, a simple threshold value may be set to determine whetherthe image has sufficient quality to proceed to the document usabilitytests.

IV. Compound IQA Scores

Embodiments of the invention use individual mobile IQA scores toautomatically create two compound IQA scores which will ensure that themobile document could be detected in the mobile image as well as thatthe document is fully legible. The first compound IQAs will be titled“Crop IQA” and “Quality IQA.”

Benefits of Compound IQAs over “individual” IQAs

Simplicity: There are more than a dozen of individual IQAs and inessence they are being replaced by 2 compounded ones: “crop” and“quality”. It's much better to move two dials than a dozen.

Eliminating wrong messages: In reality, some individual IQAs arestrongly correlated between themselves and with other defects. Forexample, shadow and darkness (2 different IQAs) may cause low OOF(another IQA) as also can small image (yet another IQA); wrong crop (hasits own IQA) can cause wrong identification and therefore present theimage as “unsupported” and so on. By compounding related IQAs we avoidpotential misclassifications and get rid of this hierarchy andthresholds altogether. MIP will have a much smaller set of thresholds(3) and the error classifier will be well-tested and optimized in R&D.

Description of Compound IQA Scores

The compound IQA scores are linear combinations of individual IQA score.The coefficients in combination depend on application as differentindividual IQA score have different importance for different documentcategories, see table 2.

V. Document Usability

Embodiments of the MDIQU system provide document usability testing toensure that the document within the image is indeed a check, a bill, adriver's license etc.

The following categories may be supported: US Checks (Mobile Deposit),Remittance Coupon (Mobile Bill Pay), Credit Card Bills (Mobile BalanceTransfer), and Driver's License (Mobile Insurance).

Definition of Critical Fields

The usability testing is based on a definition of Critical (or Required)Fields, see FIG. 1. If the fields are not specified, the following onesare used by default.

Document Application Category Field 1 Field 2 Field 3 Field 4 Field 5Field 6 Field 7 Mobile Checks MICR Legal Courtesy Endorsement DepositAmount Amount Mobile Remittance Account Payee.ZIP Biller Bill PayCoupons Number Name Balance Credit Account Payee.ZIP Biller BalanceTransfer Card Bills Number Name Due Mobile Driver DL Name Address DoBClass Sex Expiration Insurance License's Number Date

Computation of Usability Score

The usability computation involves data capture from the document imagecropped out of the mobile image. Present MDIQU system uses a MobilePreprocessing Engine and Dynamic Data Capture engine described in U.S.Pat. No. 8,379,914, which is incorporated herein by reference in itsentirety.

The Document usability score is computed based on the set of capturedcritical fields or/and the confidence of each of such fields. As withthe image quality test, the document usability tests results in a yes orno answer to the question of whether the document is usable, and willthen further analyze the response to assign a grade value to thedocument based on how usable (or un-usable) it is.

Computer-Implemented Embodiment

FIG. 7 is a block diagram that illustrates an embodiment of acomputer/server system 700 upon which an embodiment of the inventivemethodology may be implemented. The system 700 includes acomputer/server platform 701 including a processor 702 and memory 703which operate to execute instructions, as known to one of skill in theart. The term “computer-readable storage medium” as used herein refersto any tangible medium, such as a disk or semiconductor memory, thatparticipates in providing instructions to processor 702 for execution.Additionally, the computer platform 701 receives input from a pluralityof input devices 704, such as a keyboard, mouse, touch device or verbalcommand. The computer platform 701 may additionally be connected to aremovable storage device 705, such as a portable hard drive, opticalmedia (CD or DVD), disk media or any other tangible medium from which acomputer can read executable code. The computer platform may further beconnected to network resources 706 which connect to the Internet orother components of a local public or private network. The networkresources 706 may provide instructions and data to the computer platformfrom a remote location on a network 707. The connections to the networkresources 706 may be via wireless protocols, such as the 802.11standards, Bluetooth® or cellular protocols, or via physicaltransmission media, such as cables or fiber optics. The networkresources may include storage devices for storing data and executableinstructions at a location separate from the computer platform 701. Thecomputer interacts with a display 708 to output data and otherinformation to a user, as well as to request additional instructions andinput from the user. The display 708 may therefore further act as aninput device 704 for interacting with a user.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notof limitation. The breadth and scope should not be limited by any of theabove-described exemplary embodiments. Where this document refers totechnologies that would be apparent or known to one of ordinary skill inthe art, such technologies encompass those apparent or known to theskilled artisan now or at any time in the future. In addition, thedescribed embodiments are not restricted to the illustrated examplearchitectures or configurations, but the desired features can beimplemented using a variety of alternative architectures andconfigurations. As will become apparent to one of ordinary skill in theart after reading this document, the illustrated embodiments and theirvarious alternatives can be implemented without confinement to theillustrated example. One of ordinary skill in the art would alsounderstand how alternative functional, logical or physical partitioningand configurations could be utilized to implement the desired featuresof the described embodiments.

Furthermore, although items, elements or components may be described orclaimed in the singular, the plural is contemplated to be within thescope thereof unless limitation to the singular is explicitly stated.The presence of broadening words and phrases such as “one or more,” “atleast,” “but not limited to” or other like phrases in some instancesshall not be read to mean that the narrower case is intended or requiredin instances where such broadening phrases may be absent.

What is claimed is:
 1. A non-transitory computer-readable mediumcomprising instructions which, when executed by a processor, cause theprocessor to: receive a set of images associated with a document;evaluate the set of images to select an image of the set of imagessatisfying an image quality criteria; in response to the imagesatisfying the image quality criteria, process the selected image todetermine a set of image quality assurance (IQA) scores, comprising:performing a computation relating to a set of image quality assurance(IQA) scores for the selected image to obtain at least one compound IQAscore, and wherein the set of IQA scores is based on at least combininga subset of strongly correlated IQA scores in the set of IQA scores intoa compound IQA score, and wherein the subset of strongly correlated IQAscores is associated with a common image defect; in response to at leastone compound IQA score associated with the selected image of the set ofimages satisfying a predetermined standard of image quality: select theset of images as accepted.
 2. The non-transitory computer-readablemedium of claim 1, wherein to satisfy an image quality criteria, contentis at least extractable from the selected image.
 3. The non-transitorycomputer-readable medium of claim 1, wherein in response to an IQA scoreassociated with the selected image of the set of images not satisfyingthe predetermined standard of image quality the processor is further toevaluate the set of images to select another image of the set of imagessatisfying the image quality criteria.
 4. The non-transitorycomputer-readable medium of claim 1, wherein the processor is further todetermine a false selection in response to an IQA score associated withthe selected image of the set of images not satisfying the predeterminedstandard of image quality.
 5. The non-transitory computer-readablemedium of claim 4, wherein the processor is further to calculate a falseselection rate of the set of images.
 6. The non-transitorycomputer-readable medium of claim 1, wherein in response to an the setof images failing to satisfying the image quality criteria; rejectingthe set of images.
 7. The non-transitory computer-readable medium ofclaim 1, wherein the set of IQA scores includes one or more of: anout-of-focus (OOF) score, a shadow score, a small-image-size score, aview-skew score, a plain-skew score, a warpage score, alow-internal-contrast score, a reflection score, a cut-corners score, acut-sides score, a busy-background score, and a low-external-contrastscore.
 8. The non-transitory computer-readable medium of claim 1,wherein performing the computation relating to the set of IQA scores toobtain at least one compound IQA score includes computing a linearcombination of the set of IQA scores.
 9. The non-transitorycomputer-readable medium of claim 1, wherein to satisfy thepredetermined standard of image quality, the processor is further to:assess a usability score of the document associated with the set ofimages to verify that the document belongs to one of a set of supportedcategories of documents.
 10. The non-transitory computer-readable mediumof claim 9, wherein assessing the usability score of the documentincludes: capturing a set of critical fields from the image of thedocument; and determining the usability score based on the set ofcaptured critical fields.
 11. A system for assessing mobile imagequalities of a document, comprising: a processor; and a memory coupledto the processor to store instructions for assessing qualities of mobileimages of documents; wherein the processor is configured to: receive aset of images associated with a document; evaluate the set of images toselect an image of the set of images satisfying an image qualitycriteria; in response to the image satisfying the image qualitycriteria, process the selected image to determine a set of image qualityassurance (IQA) scores, comprising: performing a computation relating toa set of image quality assurance (IQA) scores for the selected image toobtain at least one compound IQA score, and wherein the set of IQAscores is based on at least combining a subset of strongly correlatedIQA scores in the set of IQA scores into a compound IQA score, andwherein the subset of strongly correlated IQA scores is associated witha common image defect; in response to at least one compound IQA scoreassociated with the selected image of the set of images satisfying apredetermined standard of image quality: select the set of images asaccepted.
 12. The system of claim 11, wherein to satisfy an imagequality criteria, content is at least extractable from the selectedimage.
 13. The system of claim 11, wherein in response to an IQA scoreassociated with the selected image of the set of images not satisfyingthe predetermined standard of image quality the processor is further toevaluate the set of images to select another image of the set of imagessatisfying the image quality criteria.
 14. The system of claim 11,wherein the processor is further to determine a false selection inresponse to an IQA score associated with the selected image of the setof images not satisfying the predetermined standard of image quality.15. The system of claim 14, wherein the processor is further tocalculate a false selection rate of the set of images.
 16. The system ofclaim 11, wherein in response to the set of images failing to satisfyingthe image quality criteria; rejecting the set of images.
 17. The systemof claim 11, wherein the set of IQA scores includes one or more of: anout-of-focus (OOF) score, a shadow score, a small-image-size score, aview-skew score, a plain-skew score, a warpage score, alow-internal-contrast score, a reflection score, a cut-corners score, acut-sides score, a busy-background score, and a low-external-contrastscore.
 18. The system of claim 11, wherein performing the computationrelating to the set of IQA scores to obtain at least one compound IQAscore includes computing a linear combination of the set of IQA scores.19. The system of claim 11, wherein to satisfy the predeterminedstandard of image quality, the processor is further to: assess ausability score of the document associated with the set of images toverify that the document belongs to one of a set of supported categoriesof documents.
 20. The system of claim 19, wherein assessing theusability score of the document includes: capturing a set of criticalfields from the image of the document; and determining the usabilityscore based on the set of captured critical fields